Predicting Crime Severity Among Individuals Deemed Not Criminally Responsible on Account of Mental Disorder (NCRMD): The Creation and Initial Validation of the Crime Severity Scale (CSS)
Notice bibliographique
Résumé
Individuals deemed Not Criminally Responsible on Account of Mental Disorder (NCRMD) typically receive indeterminant treatment sentences that fall under the authority of a provincial Review Board (RB; Crocker et al., 2015). The RB makes decisions on the conditions and length of sentences based on the risk of severe harm the individual poses to the public. RB decisions are made under consultation with forensic clinicians (e.g., psychologists and/or psychiatrists) who use violence risk assessment measures to estimate the risk of harm the individual poses to the public. However, present measures are devised to estimate risk of any future violent behaviour, without regard for the severity of the behaviour. The present study sought to improve our understanding of how to predict future crime severity to be able to better inform RBs regarding risk of harm to the public, by addressing two major objectives and research questions (RQs): (1) clarify the most salient predictors of crime severity (RQ1) and (2) if salient predictors are found in RQ1, refine current risk assessment practices by devising and psychometrically testing a scale devised to predict future crime severity, among those deemed NCRMD (RQ2). A total of 315 archived NCRMD files were coded and analyzed in the present study. Moderate-to-high interrater reliability among the three raters who coded the files was revealed. To address RQ1, postdictive methods were employed, such that historical (e.g., history of offending), social (e.g., gang affiliation), psychological (e.g., previous diagnoses), and cognitive (e.g., previous IQ) variables that would have been reasonably available to a clinician at the time someone was deemed NCRMD, were used to predict the index offence(s) that led to the NCRMD verdict. In direct test of RQ1, data was analyzed in two ways: (1) by measuring crime severity dichotomously, considering the most egregious of violent offences (i.e., homicide, attempted homicide, assault with a weapon and/or assault leading to bodily harm) as severe—and all other offences as not severe; and (2) by adopting the standards and methods employed by Statistics Canada’s Crime Severity Weights, and measuring crime severity continuously, based on sentence length. The results of the analyses revealed that the answer to what predicts crime severity, substantially depended on how crime severity was operationally defined and measured. Given the discrepancy of definitions, and the result of only one factor being predictive of crime severity, RQ2 could not be meaningfully tested in this research. The results of the present study then suggested that while rigorous efforts were employed to clarify the predictors of crime severity, research is required to clarify how to operationally define crime severity, before further investigations on predicting crime severity should be employed. Future investigations of how to operationally define crime severity are imperative, given that the RB consults with forensic clinicians to understand whether an individual deemed NCRMD poses a risk of severe harm to the public. With unclear and discrepant operational definitions of crime severity, forensic clinicians’ ability to inform RBs as to the risk of severe harm an individual poses to the public are hampered. This is concerning given that the answer to whether an individual poses a risk of severe harm to the public impacts the length, conditions, and freedoms of these indefinite treatment sentences—ultimately impacting the lives, liberty and autonomy of individuals deemed NCRMD. It also impedes the ability for forensic clinicians to support the RBs ability to balance the liberty and autonomy of the individual, with the liberty and safety of the general public. Limitations and further legal, clinical, and research implications are also discussed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».